# Quiz study guides

Author

Katie Schuler

Published

September 17, 2023

You will receive a single score (0-4, see rubric) for each topic area representing your understanding of the course material in that area. A great way to study for quizzes in general is to (1) study the lecture notes and (2) quiz yourself with the labs.

## 1 Quiz 1

Quiz 1 will test the following learning objectives, divided into 6 topic areas. For each topic area, you should be able to do the list that follows. You can think of this as a studying checklist!

1. R Basics: general
• Assign an object to a valid variable name, list all variables in the environment and remove them
• Get help with a function or package from R
• Return information about an object, including its structure, data type, length, and attributes
• Explain what functions and control flow are; differentiate between types of control flow
2. R Basics: vectors, operations, and subsetting
• Distinguish between an atomic vector and a list
• Create atomic vectors and determine their data types
• Differentiate between implicit and explicit coercion and coerce an object to another type
• Use arithmetic, comparison, and logical operators on vectors
• Explain how more complex data structures are built from atomic vectors and create them
• Distinguish between NA and NULL
• Subset vectors and higher dimensional objects with the [, [[ and \$ operators
3. Data importing
• Load the tidyverse, recognize the included packages, and critique code for redundant loading
• Construct a tidy dataset and critique whether a given dataset is tidy
• Use the map function from the purr package
• Create a tibble and distinguish between a tibble and a data frame
• Use readr to read delimited files and determine whether readr can read files of a given type
• Use col_types to add a column specifications and explain how readr guesses without it
• Solve the 3 most common importing problems we discussed in class
4. Data visualization: basics
• Describe how to create a plot with ggplot2 including the 3 basic requirements
• Distinguish between mapping and setting aesthetics
• Describe how ggplot2 maps categorical variables to aesthetics and interpret the 3 common warnings people encounter in this process
• Interpret ggplot() calls with explicit or implicit arguments for data and mapping
• Recognize the geoms we discussed in class and select which to use for a given situation
• Differentiate between globally and locally defined mappings and recognize them in given plot (or code)
5. Data visualization: layers
• Use the position argument to modify the position of the geoms in geom_bar() or geom_point()
• Describe stat="identity" and describe the default transformations for geom_bar(), geom_histogram(), and geom_smooth()
• Set the smoothing method for geom_smooth() and the bins or bindwidth for geom_histogram()
• Facet a plot with facet_wrap() and facet_grid()
• Modify axis, legend, and plot labels with labs()
• Apply a given theme to a plot and adjust the base font size or family.
• Describe scales and recognize the outcome of adding a scale layer
6. Data wrangling
• Describe the common structure of dplyr functions (aka verbs)
• Combine dplyr functions with the pipe operator to solve complex problems
• Manipulate rows with filter(), arrange(), and distinct()
• Maniuplate columns with mutate(), select(), and rename()
• Group and summarise data with group_by(), summarise(), and ungroup()
• Evaulate dplyr functions that include the common arguments we covered in class

## 2 Quiz 2

Quiz 2 will test the following learning objectives, divided into 3 topic areas. For each topic area, you should be able to do the list that follows. You can think of this as a studying checklist!

1. Sampling distribution
• Explore a dataset with an appropriate figure (histogram, boxplot, scatterplot) and summary statistics appropriate for the distribution.
• Recognize uniform and Gaussian probability distributions in a plot or equation and use R’s functions d*(), p*(), and r*() to work with these distributions
• Explain the difference between the parameter and the paramter estimate
• Construct the sampling distribution of a paramater estimate with infer and quantify the spread of the distribution with a confidence interval.
2. Hypothesis testing
• Given a set of data, implement the 3-step hypothesis testing framework nonparametrically: (1) Pose a null hypothesis, (2) quantify how likely a given pattern of results is under the null, and (3) determine whether to reject the null (conceptually and with the infer framework).
• Given a theoretical distriubiton (e.g. t), implement the 3-step hypothesis testing framework parametrically.
• Given an observed correlation, determine whether a correlation is positive, negative, or no correlation.
• Explain correlation as model building
3. Model specification
• Classify a model as supervised or unsupervised, regression or classification, and linear or nonlinear
• Identify the response and explanatory variables from a given research question or model
• Recognize the 4 ways of writing the linear model equation
• Select the equation (e.g $$y = \beta_0 + \beta_1x_1 + \beta_2x_2$$) or R expression (e.g. y ~ year + gender) for a plotted model